Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Nat Methods. 2022 Aug;19(8):950-958. doi: 10.1038/s41592-022-01560-w. Epub 2022 Aug 4.
Spatially resolved transcriptomics (SRT) provide gene expression close to, or even superior to, single-cell resolution while retaining the physical locations of sequencing and often also providing matched pathology images. However, SRT expression data suffer from high noise levels, due to the shallow coverage in each sequencing unit and the extra experimental steps required to preserve the locations of sequencing. Fortunately, such noise can be removed by leveraging information from the physical locations of sequencing, and the tissue organization reflected in corresponding pathology images. In this work, we developed Sprod, based on latent graph learning of matched location and imaging data, to impute accurate SRT gene expression. We validated Sprod comprehensively and demonstrated its advantages over previous methods for removing drop-outs in single-cell RNA-sequencing data. We showed that, after imputation by Sprod, differential expression analyses, pathway enrichment and cell-to-cell interaction inferences are more accurate. Overall, we envision de-noising by Sprod to become a key first step towards empowering SRT technologies for biomedical discoveries.
空间分辨转录组学（SRT）在保留测序物理位置的同时，提供了接近甚至优于单细胞分辨率的基因表达水平，并且通常还提供了匹配的病理图像。然而，SRT 表达数据由于每个测序单元的浅层覆盖以及保留测序位置所需的额外实验步骤而存在较高的噪声水平。幸运的是，通过利用测序的物理位置信息以及对应病理图像中反映的组织结构，这种噪声可以被去除。在这项工作中，我们基于匹配的位置和成像数据的潜在图学习，开发了 Sprod，用于推断准确的 SRT 基因表达。我们全面验证了 Sprod，并展示了它在去除单细胞 RNA 测序数据中的缺失值方面优于先前方法的优势。我们表明，通过 Sprod 插补后，差异表达分析、通路富集和细胞间相互作用推断更加准确。总的来说，我们设想通过 Sprod 去噪将成为为生物医学发现赋能 SRT 技术的关键第一步。
